Predicting the effectiveness of hepatitis C virus neutralizing antibodies by bioinformatic analysis of conserved epitope residues using public sequence data

Hepatitis C virus (HCV) is a global health issue. Although direct-acting antivirals are available to target HCV, there is currently no vaccine. The diversity of the virus is a major obstacle to HCV vaccine development. One approach toward a vaccine is to utilize a strategy to elicit broadly neutralizing antibodies (bNAbs) that target highly-conserved epitopes. The conserved epitopes of bNAbs have been mapped almost exclusively to the E2 glycoprotein. In this study, we have used HCV-GLUE, a bioinformatics resource for HCV sequence data, to investigate the major epitopes targeted by well-characterized bNAbs. Here, we analyze the level of conservation of each epitope by genotype and subtype and consider the most promising bNAbs identified to date for further study as potential vaccine leads. For the most conserved epitopes, we also identify the most prevalent sequence variants in the circulating HCV population. We examine the distribution of E2 sequence data from across the globe and highlight regions with no coverage. Genotype 1 is the most prevalent genotype worldwide, but in many regions, it is not the dominant genotype. We find that the sequence conservation data is very encouraging; several bNAbs have a high level of conservation across all genotypes suggesting that it may be unnecessary to tailor vaccines according to the geographical distribution of genotypes

DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

A VERY FAST CONSTRAINT SOLVER INTERPRETER FOR EVALUATING MODEL CONSTRAINTS

Poster and poster abstractModel-Driven Engineering (MDE) facilitates building solutions in many enterprise application domains through the systematic use of graphical languages called domain-specific modeling languages (DSMLs). MDE tools, such as the Generic Modeling Environment (GME) and the Generic Eclipse Modeling System (GEMS), enable end-users to rapidly create such custom DSMLs. One advantage of using DSMLs is its correct-by-construction characteristics, which is provided by domain-specific constraints defined within these custom languages. The constraints, written in Object Constraint Language (OCL), are evaluated during and after model construction using a constraint checker. For example, GME provides a Constraint Manager (CM) that evaluate the constraints defined by a DSMLs against its models. Unfortunately, our experience has shown that the constraint checkers provided by MDE tools do not scale to large models (i.e., models that have 10s of 1000s of model elements and 10s of 100s of constraints). Our research therefore focuses on developing a very fast OCL constraint solver that can address the current shortcomings of existing OCL constraint solvers in the context of GME. Our design approach leverages best practices in software design patterns, caching, and multi-threading to improve its performance and scalability. Initial results of our work show that for small models (e.g., 10s to 100s of elements), the traditional constraint solvers run slightly faster than our approach. For models with more than 1000s of elements, our approach is twice as fast, and performs exponential better as the size and complexity of the models increase

Understanding the Genetics of Clubroot Resistance for Effectively Controlling this Disease in Brassica Species

Clubroot disease is one of the most serious diseases of Brassica species, which is caused by soil-borne pathogen Plasmodiophora brassicae Woronin. Clubroot disease has a long history on vegetable crops belonging to the Brassica species; most recently, this disease is also invading rapeseed/canola crop around the globe. The clubroot disease causes significant yield and quality losses in highly infected fields. Clubroot pathogens invade into the host plant roots and infect root tissues with the formation of abnormal clubs, named as galls, which results in incompetent plant roots to intake water and nutrients and eventually dead plants. As it is a soil-borne disease and accomplishes its disease cycle in two different phases and both phases are highly efficient to damage root system as well as to release more inoculum, there are many challenges to control this disease through chemical and other cultural practices. In general, clubroot disease can be effectively managed by developing resistant cultivars. In this chapter, various resistance sources of clubroot disease in different Brassica species have been discussed with potential applications in canola/rapeseed breeding programs worldwide. Importance of gene mapping and molecular marker development efforts by different research studies for clubroot in B. rapa, B. oleracea, and B. napus has been stressed. Transcriptomic and metabolomic changes occurring during host–pathogen interactions are also covered in this chapter, which would enhance our understanding and utilization of clubroot resistance in Brassica species

Recombinant Flag-tagged E1E2 glycoproteins from three hepatitis C virus genotypes are biologically functional and elicit cross-reactive neutralizing antibodies in mice

Hepatitis C virus (HCV) is a globally disseminated human pathogen for which no vaccine is currently available. HCV is highly diverse genetically and can be classified into 7 genotypes and multiple sub-types. Due to this antigenic variation, the induction of cross-reactive and at the same time neutralizing antibodies is a challenge in vaccine production. Here we report the analysis of immunogenicity of recombinant HCV envelope glycoproteins from genotypes 1a, 1b and 2a, with a Flag tag inserted in the hypervariable region 1 of E2. This modification did not affect protein expression or conformation or its capacity to bind the crucial virus entry factor, CD81. Importantly, in immunogenicity studies on mice, the purified E2-Flag mutants elicited high-titer, cross-reactive antibodies that were able to neutralize HCV infectious particles from two genotypes tested (1a and 2a). These findings indicate that E1E2-Flag envelope glycoproteins could be important immunogen candidates for vaccine aiming to induce broad HCV-neutralizing responses

Disentanglement by Dissipative Open System Dynamics

This paper investigates disentanglement as a result of evolution according to a class of master equations which include dissipation and interparticle interactions. Generalizing an earlier result of Di\'{o}si, the time taken for complete disentanglement is calculated (i.e. for disentanglement from any other system). The dynamics of two harmonically coupled oscillators is solved in order to study the competing effects of environmental noise and interparticle coupling on disentanglement. An argument based on separability conditions for gaussian states is used to arrive at a set of conditions on the couplings sufficient for all initial states to disentangle for good after a finite time.Comment: Paper in conjunction with and following on from P.J. Dodd and J.J. Halliwell: quant-ph/031206

Nondeterministic Instance Complexity and Proof Systems with Advice

Motivated by strong Karp-Lipton collapse results in bounded arithmetic, Cook and Krajíček [1] have recently introduced the notion of propositional proof systems with advice. In this paper we investigate the following question: Given a language L , do there exist polynomially bounded proof systems with advice for L ? Depending on the complexity of the underlying language L and the amount and type of the advice used by the proof system, we obtain different characterizations for this problem. In particular, we show that the above question is tightly linked with the question whether L has small nondeterministic instance complexity

Extremal extensions of entanglement witnesses: Unearthing new bound entangled states

In this paper, we discuss extremal extensions of entanglement witnesses based on Choi's map. The constructions are based on a generalization of the Choi map due to Osaka, from which we construct entanglement witnesses. These extremal extensions are powerful in terms of their capacity to detect entanglement of positive under partial transpose (PPT) entangled states and lead to unearthing of entanglement of new PPT states. We also use the Cholesky-like decomposition to construct entangled states which are revealed by these extremal entanglement witnesses.Comment: 8 pages 6 figures revtex4-